Sensor array noise reduction

ABSTRACT

Sensor array noise reduction is provided. Techniques involve systems and methods for reducing sensor array noise by estimating an instantaneous frequency of a coherent noise signal in a logging system. The instantaneous frequency may be estimated based on a rotation of an electric motor in the logging system, or may estimated by applying suitable signal processing algorithms on measurements in the logging system. A virtual measurement may be computed using the instantaneous frequency. The virtual measurement may be demodulated to obtain a baseband signal, and noise may be removed from the baseband signal to obtain a denoised signal.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. ProvisionalApplication No. 62/357,020, filed on Jun. 30, 2016, the entirety ofwhich is incorporated herein by reference.

BACKGROUND

In electromagnetic telemetry, the presence of noise from unwantedelectromagnetic sources can threaten the reliability of a telemetryuplink. Such noise can be generated by a wide variety of devicesassociated with electromagnetic energy including mud motors, wellheadequipment, AC units, vehicles, welding equipment, consumer electronics(including residential microwave appliances), etc.

SUMMARY

Sensor array noise reduction is provided. Techniques involve systems andmethods for reducing sensor array noise by estimating an instantaneousfrequency of a coherent noise signal in a logging system. Theinstantaneous frequency may be estimated based on a rotation of anelectric motor in the logging system, or may estimated by applyingsuitable signal processing algorithms on measurements in the loggingsystem. A virtual measurement may be computed using the instantaneousfrequency. The virtual measurement may be demodulated to obtain abaseband signal, and noise may be removed from the baseband signal toobtain a denoised signal.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used as an aid inlimiting the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be morereadily understood by reference to the following description taken inconjunction with the accompanying drawings.

FIG. 1 illustrates an example wellsite in which embodiments of sensorarray noise reduction can be employed;

FIG. 2 illustrates an example global uplink chain that can be used withimplementations of sensor array noise reduction;

FIG. 3 illustrates an example observation model in accordance withimplementations of sensor array noise reduction;

FIG. 4 illustrates example constellation centers which might be expectedin a QPSK modulation in accordance with various implementations ofsensor array noise reduction;

FIG. 5 illustrates an example probability density function of a cleantelemetry signal where no noise is present in accordance with variousimplementations of sensor array noise reduction;

FIG. 6 illustrates an example probability distribution function of atelemetry signal corrupted by noise in accordance with variousimplementations of sensor array noise reduction;

FIG. 7 illustrates example metrics for PSK modulations including annularvariance dispersion metrics in accordance with various implementationsof sensor array noise reduction;

FIG. 8 illustrates example metrics for PSK modulations including angularvariance dispersion metrics in accordance with various implementationsof sensor array noise reduction;

FIG. 9 illustrates example metrics for PSK modulations includingconstellation variance dispersion metrics in accordance with variousimplementations of sensor array noise reduction;

FIG. 10 illustrates an example method associated with sensor array noisereduction;

FIG. 11 illustrates an example method associated with sensor array noisereduction;

FIG. 12 illustrates an example method associated with sensor array noisereduction;

FIG. 13 illustrates an example global uplink chain using virtualmeasurements for sensor array noise reduction;

FIG. 14 illustrates a spectrogram having coherent noise;

FIG. 15 illustrates a graph of instantaneous frequency of coherentnoise;

FIG. 16 illustrates a spectrogram of an in-phase virtual measurement;and

FIGS. 17A and 17B illustrate example computing devices that can be usedin accordance with various implementations of sensor array noisereduction.

DETAILED DESCRIPTION

In the following description, numerous details are set forth to providean understanding of some embodiments of the present disclosure. However,it will be understood by those of ordinary skill in the art that thesystem and/or methodology may be practiced without these details andthat numerous variations or modifications from the described embodimentsmay be possible.

As described herein, various techniques and technologies can facilitatethe reduction of the influence of undesired noise sources inelectromagnetic signals, such as for example, in improving a signal tonoise ratio in an electromagnetic telemetry system. It will beunderstood that the term “noise reduction” as used herein includes arange of signal noise reduction, from decreasing some of the noise in asignal to cancellation of noise in a signal.

In one possible implementation, sensor array noise reduction can beaccomplished through the use of multiple sensors in conjunction with asensor array noise reduction manager utilizing a demixing vector. In onepossible aspect, N sensors can be used to process N−1 noise sources froma desired signal. In another possible aspect, different noise sourcescan be jointly removed rather than sequentially removed from the desiredsignal.

In one possible implementation, sensor array noise reduction can be usedin conjunction with Electromagnetic (EMAG) telemetry, includingscenarios where EMAG telemetry is employed in conjunction with MeasuringWhile Drilling (MWD)/Logging While Drilling (LWD) operations and/or inunderbalanced drilling conditions, and/or when gas is used instead ofmud as drilling fluid.

In some implementations, embodiments of sensor array noise reduction canbe used to reduce environmental noise in EMAG telemetry and improve areliability of an associated uplink telemetry. This can includescenarios in which EMAG telemetry power constraints result in a signalpower measured at a well surface which is smaller than environmentalnoise present at a wellsite.

FIG. 1 illustrates a wellsite 100 in which embodiments of sensor arraynoise reduction can be employed. Wellsite 100 can be onshore oroffshore. In this example system, a borehole 102 is formed in asubsurface formation by rotary drilling in a manner that is well known.Embodiments of sensor array noise reduction can also be employed inassociation with wellsites where directional drilling is beingconducted.

A drill string 104 is suspended within the borehole 102 and has a bottomhole assembly 106 which includes a drill bit 108 at its lower end. Thesurface system includes platform and derrick assembly 110 positionedover the borehole 102. The assembly 110 can include a rotary table 112,kelly 114, hook 116 and rotary swivel 118. The drill string 104 isrotated by the rotary table 112, energized by means not shown, whichengages the kelly 114 at an upper end of the drill string 104. The drillstring 104 is suspended from the hook 116, attached to a traveling block(also not shown), through the kelly 114 and a rotary swivel 118 whichpermits rotation of the drill string 104 relative to the hook 116. As iswell known, a top drive system can also be used.

In the example of this embodiment, the surface system can furtherinclude drilling fluid or mud 120 stored in a pit 122 formed at thewellsite 100. A pump 124 delivers the drilling fluid 120 to the interiorof the drill string 104 via a port in the swivel 118, causing thedrilling fluid 120 to flow downwardly through the drill string 104 asindicated by the directional arrow 126. The drilling fluid 120 exits thedrill string 104 via ports in the drill bit 108, and then circulatesupwardly through the annulus region between the outside of the drillstring and the wall of the borehole 102, as indicated by the directionalarrows 128. In this well-known manner, the drilling fluid 120 lubricatesthe drill bit 108 and carries formation cuttings up to the surface asthe drilling fluid 120 is returned to the pit 122 for recirculation.

The bottom hole assembly 106 of the illustrated embodiment can includedrill bit 108 as well as a variety of equipment 130, including alogging-while-drilling (LWD) module 132, a measuring-while-drilling(MWD) module 134, a roto-steerable system and motor, various othertools, etc.

In one possible implementation, the LWD module 132 can be housed in aspecial type of drill collar, as is known in the art, and can includeone or more of a plurality of known types of logging tools (e.g., anuclear magnetic resonance (NMR system), a directional resistivitysystem, and/or a sonic logging system). It will also be understood thatmore than one LWD and/or MWD module can be employed (e.g. as representedat 136). (References, throughout, to a module at the position of 132 canalso mean a module at the position of 136 as well.) The LWD module 132can include capabilities for measuring, processing, and storinginformation, as well as for communicating with the surface equipment.

The MWD module 134 can also be housed in a special type of drill collar,as is known in the art, and include one or more devices for measuringcharacteristics of the well environment, such as characteristics of thedrill string and drill bit. The MWD tool can further include anapparatus (not shown) for generating electrical power to the downholesystem. This may include a mud turbine generator powered by the flow ofthe drilling fluid 120, it being understood that other power and/orbattery systems may be employed. The MWD module 134 can include one ormore of a variety of measuring devices known in the art including, forexample, a weight-on-bit measuring device, a torque measuring device, avibration measuring device, a shock measuring device, a stick slipmeasuring device, a direction measuring device, and an inclinationmeasuring device.

Various embodiments of the present disclosure are directed to systemsand methods for transmitting information (data and/or commands) fromequipment 130 to a surface 138 of the wellsite 100. In oneimplementation, the information can be received by one or more sensors140. The sensors 140 can be located on, above, or below the surface 138in a variety of locations. In one possible implementation, placement ofsensors 140 can be independent of precise geometrical considerations.Sensors 140 can be chosen from any sensing technology known in the art,including those capable of measuring electric or magnetic fields,including electrodes (such as stakes), magnetometers, coils, etc.

In one possible implementation, the sensors 140 receive informationincluding LWD data and/or MWD data, which can be utilized to steer thedrill 108 and any tools associated therewith. In one implementation theinformation received by the sensors 140 can be filtered to decreaseand/or cancel noise at a logging and control system 142. Logging andcontrol system 142 can be used with a wide variety of oilfieldapplications, including logging while drilling, artificial lift,measuring while drilling, etc. Also, logging and control system 142 canbe located at surface 138, below surface 138, proximate to borehole 102,remote from borehole 102, or any combination thereof.

Alternately, or additionally, the information received by the sensors140 can be filtered to decrease and/or cancel noise at one or more otherlocations, including any configuration known in the art, such as in oneor more handheld devices proximate and/or remote from the wellsite 100,at a computer located at a remote command center, in the logging andcontrol system 142 itself, etc.

Noise Reduction with Sensor Measurements

FIG. 2 illustrates an example global uplink chain 200 that can be usedin conjunction with implementations of sensor array noise reduction. Inone possible implementation, information 202 is collected or produced byequipment, such as equipment 130. In one possible aspect, information202 can be represented as binary information.

Information 202 can be modulated at a modulator 204 and transmitted to ademodulator 206. In one possible embodiment, modulator 204 produces asignal 208, such as an electromagnetic signal including information 202that can be transmitted using any method and equipment known in the art.Signal 208 can be susceptible to one or more noise sources 210 duringtransmission. Noise sources 210 can include a wide variety of devicesassociated with electromagnetic energy such as, for example, mud motors,well heads, AC units, vehicles, welding operations, consumerelectronics, electric perturbations from external sources for which nodirect mitigation can be achieved, etc.

In one possible implementation, signal 208 with accompanying noise isreceived by sensors, such as sensors 140. The sensors providemeasurements 212 corresponding to signal 208 with accompanying noise, todemodulator 206. Signal 208 with accompanying noise from noise sources210, is demodulated at demodulator 206. In one possible aspect, a sensorarray noise reduction manager 214 can be employed to apply the conceptsof sensor array noise reduction to remove or reduce noise fromdemodulated signal 208 to produce a denoised signal. Decoded information203 can be obtained from the denoised signal by a symbol estimator 216using any symbol estimation techniques known in the art.

Example Observation Model

FIG. 3 illustrates an example observation model 300 in accordance withimplementations of sensor array noise reduction. As shown, fourelectromagnetic sources 302, 304, 306, and 308 are present, though itwill be understood that more or fewer electromagnetic sources can alsobe used. Electromagnetic sources 302-308 can be represented by “so₁(t)”, “so₂(t)”, “so₃(t)” and “so₄(t)”, respectively, where t is thetime.

In one possible implementation, source 302 can be a telemetry sourceproducing a signal to be extracted while sources 304-308 can be noisesources. Measurement of the signal from source 302 can be achieved usingsensors 140, such as metal rods, coils, magnetometers, or anymeasurement device sensitive to an electric or magnetic field. In onepossible implementation, the measurements can be obtained byamplification of the difference of electric potential measured between a“ref” sensor 310 (denotable as ref(t)) and other sensors 312, 314, 316,318 (which can be denoted respectively as “se₁(t)”, “se₂(t)”, “se₃(t)”,“se₄(t)”) such that a voltage v_(i)(t) measured at surface 138 can beproportional to a difference of potential v_(i)(t)=G.(se_(i)(t)−ref(t)), where G is a measurement gain.

In one possible implementation, any signal obtained at surface 138 whichis proportional to the electric or magnetic field on a surface locationor proportional to the difference of the electric field or magneticfield between two surface locations can be denoted as v_(i)(t).

In one possible aspect, according to the superposition principle, therelationship between the signals measured and the sources can be writtenas the following linear relationship:

$\begin{bmatrix}{v_{1}(t)} \\\vdots \\{v_{i}(t)}\end{bmatrix} = {\begin{bmatrix}m_{11} & \ldots & m_{1j} \\\vdots & \; & \vdots \\m_{i\; 1} & \ldots & m_{ij}\end{bmatrix}\begin{bmatrix}{{so}_{1}(t)} \\\vdots \\{{so}_{j}(t)}\end{bmatrix}}$

If the mixing matrix [m_(ij)] is invertible, the sources can berecovered using the inverse matrix (or pseudoinverse in the case i>j) asfollows:

$\begin{matrix}{\begin{bmatrix}{{so}_{1}(t)} \\\vdots \\{{so}_{j}(t)}\end{bmatrix} = {\begin{bmatrix}m_{11} & \ldots & m_{1j} \\\vdots & \; & \vdots \\m_{i\; 1} & \ldots & m_{ij}\end{bmatrix}^{+}\begin{bmatrix}{v_{1}(t)} \\\vdots \\{v_{i}(t)}\end{bmatrix}}} \\{= {\begin{bmatrix}d_{11} & \ldots & d_{1i} \\\vdots & \; & \vdots \\d_{j\; 1} & \ldots & d_{ji}\end{bmatrix}\begin{bmatrix}{v_{1}(t)} \\\vdots \\{v_{i}(t)}\end{bmatrix}}}\end{matrix}$

In one possible embodiment, the symbol “+” can denote either the inversematrix (if i=j) or the pseudoinverse matrix (if i>j). In one possibleimplementation, the matrix [d_(ji)] can be called the demixing matrix.

In one possible embodiment, the electromagnetic source so₁ can berecovered using the different measurements v_(i), the first line[d_(1j)] of the demixing matrix, and the source so₁ (t) can be recoveredusing following equation

${{so}_{1}(t)} = {\sum\limits_{k = 1}^{i}{d_{1k} \cdot {v_{k}(t)}}}$

The vector [d_(1i)] can be referred to as the “demixing vector”.

Example Problem Formulation

In one possible implementation, at surface 138 one or more measurementsv_(i)(t) from sensors 140 can be converted to a constellation spaceusing demodulation (such as lowpass filtering and/or downsampling) atthe rate of one sample per symbol. The samples obtained from themeasurement v_(i) (t) at the end of this procedure can be denotedz_(i)[n] where n is the symbol index.

For example, in the constellation domain, the samples of the telemetrysignal may be concentrated around the constellation centers of themodulation.

FIG. 4 shows example constellation centers 400 which might be expectedin one implementation of sensor array noise reduction for a QuadraturePhase-Shift Keying (QPSK) modulation. In FIG. 4, four constellationcenters 400 are shown, however it will be understood that more or lessconstellation centers can also be used.

FIG. 5 illustrates an example probability density function 500 of aclean signal, such as a telemetry signal, where no noise is present inaccordance with implementations of sensor array noise reduction. Symbols502, 504, 506, 508 are distributed in the neighborhood of theconstellation centers 400.

FIG. 6 illustrates an example probability distribution function 600 of asignal corrupted by noise in accordance with implementations of sensorarray noise reduction. Symbols 602, 604, 606, 608 are spread around theconstellation centers 400, causing a number of symbol errors.

In one possible implementation, the dispersion of the symbols around thetheoretical constellation centers 400 can be measured quantitativelyusing one or more statistical metrics, such as variance, standarddeviation and/or higher order statistics. Examples of such metrics forPhase-Shift Keying (PSK) modulations include annular variance 700,angular variance 800 and constellation variance 900 dispersions metricswhich are shown in FIG. 7, FIG. 8 and FIG. 9, respectively. In onepossible embodiment, given a signal z_(i)[n], for any modulation it maybe possible to define a statistical dispersion metric 700, 800, 900referred as disp (z_(i) [n]).

As an illustrative example of one possible implementation, the biasedannular variance of a telemetry signal z_(i) [n] modulated using amember of the PSK family may be given by:

$\begin{matrix}{\sigma = {{disp}\left( {z_{i}\lbrack n\rbrack} \right)}} \\{= \sqrt{\sum\limits_{n}\left( {{{z_{i}\lbrack n\rbrack}} - 1} \right)^{2}}}\end{matrix}$

In one possible implementation, noise reduction in, for example, EMAGtelemetry can be formulated as the following reduction and/orminimization exercise under constraint:

$\left\{ {\begin{matrix}{\left\lbrack {\mspace{11mu}\ldots\mspace{14mu}}\; \right\rbrack = {\underset{\lbrack{d_{1}\;\ldots\mspace{11mu} d_{i}}\rbrack}{\arg\;\min}\;{{disp}\left( {\sum\limits_{k = 1}^{i}{d_{k} \cdot {z_{k}\lbrack n\rbrack}}} \right)}}} \\{{{under}\mspace{14mu}{the}\mspace{14mu}{constraint}\mspace{14mu}{{\sum\limits_{k = 1}^{i}{d_{k} \cdot {z_{k}\lbrack n\rbrack}}}}} = c}\end{matrix}\quad} \right.$where the constant c can be set to c=1. In one possible aspect, thedemixing vector can be solved for to decrease the dispersion of thesymbols 602, 604, 606, 608 around the constellation centers 400. In onepossible aspect, this global search can be solved using one or more ofnumerous local or global optimization algorithms, including:

-   -   Deterministic or stochastic gradient descent    -   Monte-carlo minimization    -   Bayesian optimization

Once the demixing vector is found at the end of the global procedure(which in one aspect may be run towards optimization), in one possibleaspect the denoised symbols z_(d) [n] can be obtained using thefollowing equation

${z_{d}\lbrack n\rbrack} = {\sum\limits_{k = 1}^{i}{\; \cdot {z_{k}\lbrack n\rbrack}}}$Example Reduction of Search Space

In one possible embodiment, it may be possible to use the constraintterm |Σ_(k=1) ^(i)d_(k)·z_(k) [n]|=c to reduce the dimension of thesearch space to improve the efficiency of the global search. In onepossible aspect, an observation matrix Z can be defined as

$Z = \begin{bmatrix}{z_{1}\lbrack n\rbrack} & \ldots & {z_{1}\left\lbrack {n + m} \right\rbrack} \\\vdots & \; & \vdots \\{z_{i}\lbrack n\rbrack} & \ldots & {z_{i}\left\lbrack {n + m} \right\rbrack}\end{bmatrix}$

And the demixing vector d can be defined asd=[d ₁ . . . d _(i)]

With these notations, one can write

${{\sum\limits_{k = 1}^{i}{d_{k} \cdot {z_{k}\lbrack n\rbrack}}}}^{2} = {{dZZ}^{T}d^{T}}$

In one implementation the matrix ZZ^(T) can be symmetric and bedecomposed using singular value decomposition such thatZZ ^(T)=(S√{square root over (V)})(S√{square root over (V)})^(T)

Where V is the diagonal matrix containing the positive singular valuesof ZZ^(T) and where S is a matrix with same size as Z. The number ofelements on the diagonal can be reduced by keeping the r singular valueshigher than some threshold, and the reduced matrix containing theseelements can be denoted as V_(r). In a similar fashion, the reducedmatrix S_(r) can include the first r columns of the matrix S.

In one possible implementation, one assumption behind the use of thereduced matrices V_(r) and S_(r) can be that if the threshold is smallenough, then one can show thatd˜d _(n)·(S _(r) √{square root over (V)} _(r))⁺

In one aspect, the normalized vector d_(n) of size r can verify thecondition d_(n)·d_(n) ^(T)=c which defines the equation of anr-dimensional sphere with radius c. One or more points at the surface ofan r-dimensional sphere with constant radius can be expressed using rdistinct Cartesian coordinates and/or indifferently by using (r−1)spherical coordinates. Defining the spherical coordinates vector Φ=[(ϕ₁. . . ϕ_(r-1)], ϕ₁ . . . ϕ_(r-1)∈[0,2π[and T(·) as the spherical toCartesian transformation such that d_(n)=T(Φ), the initial searchproblem can be rewritten as follows

$\left\{ {\begin{matrix}{\hat{\Phi} = {\min\limits_{\Phi}\;{{disp}\left( {{T(\Phi)} \cdot \left( {S_{r}\sqrt{V_{r}}} \right)^{+}} \right)}}} \\{{\lbrack\rbrack} = {T\left( \hat{\Phi} \right)}}\end{matrix}\quad} \right.$

With this reduced formulation, the size of the search space can have(r−1) variables while the initial formulation can have i differentvariables.

Example of Sparse Decomposition Process

In some implementations, more than one demixing vector can be found forthe same observation matrix. In such cases, a global reduction(including potentially a minimum) can be estimated during the searchphase along with various different local lows (including minimums). Thiscan be done using, for example:

A brute force process—During the search phase, the potential vectorcandidates can be tested and vectors corresponding to a decrease(including a minima) of the dispersion metric can be kept in a listwhile other vectors can be discarded

a basis pursuit process—A search of a global decrease can be carried outin accordance with known procedures in the art. Once the demixing vectoris found, the inner product α_(i)=

z_(i)[n], z_(d)[n]

between the denoised signal z_(d)[n] and the signal z_(i)[n] can beestimated. The residual z_(i) ^(r)[n]=z_(i)[n]−α_(i)z_(d) [n] can beestimated for each signal z_(i) [n], and the residual observation matrixZ^(r) can formed. A new search of the global high (including a possiblemaximum) can be carried out using the residual Z^(r) instead of Z. Inone possible implementation, this process of estimation-subtraction canbe iterated until a desired decomposition order is achieved.

Example Methods

FIGS. 10-12 illustrate example methods for implementing aspects ofsensor array noise reduction. The methods are illustrated as acollection of blocks and other elements in a logical flow graphrepresenting a sequence of operations that can be implemented inhardware, software, firmware, various logic or any combination thereof.The order in which the methods are described is not intended to beconstrued as a limitation, and any number of the described method blockscan be combined in any order to implement the methods, or alternatemethods. Additionally, individual blocks and/or elements may be deletedfrom the methods without departing from the spirit and scope of thesubject matter described therein. In the context of software, the blocksand other elements can represent computer instructions that, whenexecuted by one or more processors, perform the recited operations.Moreover, for discussion purposes, and not purposes of limitation,selected aspects of the methods may described with reference to elementsshown in FIGS. 1-9.

It will be understood that computations in sensor array noise reduction,including those discussed in FIGS. 10-12, can be done in baseband and/orat the rate of carrier's frequency. Further, it will be understood thata variety of frame structures and error correcting codes known in theart can be used. Further, a nature of modulation used may also beaccounted for, i.e. a probability density function of the modulation canbe utilized to provide information to discriminate a desired signal fromnoise in sensor array noise reduction. Moreover a linear combination ofall measurements made, such as measurements made by sensors 140, may beused in sensor array noise reduction to generate a denoised signal.

FIG. 10 illustrates an example data learning method 1000 that can beused with embodiments of sensor array noise reduction. As shown, anobservation matrix Z can be formed from samples 1002 of signals z_(i)[n]1004, such as signals 208. Signals 1004 can include, for example,information received by sensors 140 and can already have beendemodulated, such as by demodulator 206. In one implementation, matrix Zcan be formed by taking at least some consecutive samples 1002. In onepossible embodiment, a sliding window can be employed to access samples1002 for use in estimating denoising parameters. In one aspect, thesamples 1002 correspond in time (i.e. the samples are associated withmeasurements made by sensors 140 during the same time frame).

At block 1006, a dispersion metric such as disp(z_(i)[n]), can beaccessed and estimated. In one possible implementation, the dispersionmetric can be estimated for one or more demixing vectors, such asdemixing vectors {d_i}, in a demixing vector database, such as database1008. This can include examining a spread of samples 1002 relative toconstellations centers, such as centers 400. In one possible aspect, alist of demixing vectors used in previous sensor array noise reductionactivities saved in demixing vector database 1008 can be utilized.

At block 1010, if the value of a dispersion metric is below a presetthreshold (such as a performance threshold), a suitable demixing vector,such as demixing vector d_(k), providing an acceptable dispersion metricvalue can be selected at block 1012. In one possible embodiment, thedemixing vector d_(k) selected can be one providing a dispersion metricvalue below a preset threshold. In one possible aspect this can includeselecting d_(k) to minimize disp(d_(k)·Z).

If the value of the dispersion metric examined at block 1010 is above apreset threshold, at block 1014 one or more new demixing vectors can beestimated using matrix Z. The one or more new demixing vectors can becalculated using a variety of methods known in the art including, forexample, global search and sparse decomposition processes (includingbrute force and basis pursuit). In one possible aspect, complete and/orreduced formulations can be used in these calculations.

In one possible implementation, demixing vector database 1008 can beupdated to include at least some of the new vectors calculated at block1014. In one aspect, older vectors can be removed from database 1008,such as, for example, when new vectors are added to database 1008.

At block 1016, denoised symbols Z_(d)=d_(k)Z can be calculated using,for example, one or more demixing vectors d_(k) selected at block 1012and/or computed at block 1014. In one possible implementation, denoisedsymbols Z_(d)=d_(k)Z can be decoded by a telemetry receiver configuredto decode data symbols, such as symbol estimator 216.

It will be understood that in one possible embodiment, a loop can beformed between blocks 1006, 1010, 1014 and 1008 allowing method 1000 toevaluate one or more of the demixing vectors in database 1008. If ademixing vector cannot be found in database 1008 that produces adispersion metric below the preset threshold in block 1010, method 1000can compute a new demixing vector at 1014. Method 1000 can then selectthe newly calculated demixing vector, such as at block 1012, if thenewly calculated demixing vector produces a dispersion metric that isbetter than the dispersion metrics produced by the demixing vectorsevaluated from database 1008. In one aspect, to avoid an infinite loopbetween 1006-1014, the newly calculated demixing vector can be selectedas the suitable demixing vector when the demixing vectors evaluated(including the newly calculated demixing vector and the demixing vectorsfrom database 1008) produce dispersion metrics above the presetthreshold. In such a case the newly calculated demixing vector ispresented to block 1016.

It will also be understood that in some possible implementations, one ormore demixing vectors can be calculated on the fly without querying fordemixing vectors from database 1008. For example, in one aspect, oninitial startup, database 1008 may be empty, so method 1000 may godirectly to 1014 to compute one or more new demixing vectors. In anotherpossible aspect, database 1008 may be absent or inaccessible. In anotherpossible aspect, method 1000 may choose to ignore database 1008 andcalculate new demixing vectors rather than query for demixing vectorsfrom database 1008.

It will be further understood that In one possible embodiment, one ormore demixing vectors can be applied to samples 1002 that correspond intime.

FIG. 11 illustrates an example method 1100 for selecting and using ademixing vector in accordance with implementations of sensor array noisereduction.

At block 1102, an existing demixing vector, such as demixing vector{d_i}, is queried from a database, such as demixing vector database1008.

At block 1104, the existing demixing vector is examined to confirm thatit produces an acceptable dispersion metric. In one possibleimplementation, the demixing vector can be examined to confirm that itproduces an acceptable dispersion metric with a set of signal samples,such as samples 1002. Stated another way for the sake of clarity, thedemixing vector can be applied to portions of signals from a variety ofsensors that correspond in time (i.e. portions that were produced by thesensors during the same time frame). In one possible embodiment, anacceptable dispersion metric is one that falls below a preset threshold.

At block 1106, once the existing demixing vector is confirmed to producean acceptable dispersion metric, the existing demixing vector is used asa selected demixing vector to filter noise contributed by noise sources,such as noise sources 210, from a signal, such as signal 1004, to arriveat a clearer signal, such as signal 208. In one implementation, thedemixing vector improves a signal to noise ratio in the signal.

FIG. 12 illustrates another example method 1200 in accordance withsensor array noise reduction.

At block 1202, a new demixing vector is calculated. In one possibleimplementation, the new demixing vector can be calculated on the flywithout previously querying for demixing vectors from a database, suchas database 1008. In one implementation, the new demixing vector can beestimated using matrix Z and be calculated using operations similar tothose discussed above in conjunction with block 1014. For example, thenew demixing vector can be calculated using a variety of methods knownin the art including, for example, global search and sparsedecomposition processes (including brute force and basis pursuit). Inone possible aspect, complete and/or reduced formulations can be used.

At block 1204 the new demixing vector can be used as a selected demixingvector to filter noise sources from a signal being denoised, such assignal 1004. For example, the demixing vector can be applied to portionsof signals, such as samples 1002, from a variety of sensors, such assensors 140, wherein the samples correspond in time (i.e. the samplesare portions of signals that were produced by the sensors during thesame time frame). In one implementation, the new demixing vector can besaved to a database, such as demixing vector database 1008, for possibleuse in future denoising operations.

Noise Reduction with Virtual Measurements

FIG. 13 illustrates an example global uplink chain 1210 that includemultiple types of sensor array noise reduction and/or can be used inconjunction with various implementations of sensor array noisereduction. For example, while the global uplink chain 200 presented inFIG. 2 described implementations of sensor array noise reduction usingmeasurements 212 measured at the sensors 140, in accordance with thepresent techniques, sensor array noise reduction may also involvegenerating virtual measurements for noise reduction. Techniques forreducing noise using virtual measurements may be used alone or incombination with other techniques, such as sensor array noise reductionusing sensor measurements.

In one embodiment as illustrated in global uplink chain 1210,information 202 is collected or produced by equipment, such as equipment130. In some embodiments, information 202 can be represented as binaryinformation and may be modulated at a modulator 204 before transmission.For example, modulator 204 produces a signal 208, such as anelectromagnetic signal including information 202 that can be transmittedusing any method and equipment known in the art. Signal 208 can besusceptible to one or more noise sources 210 during transmission. Noisesources 210 can include a wide variety of devices associated withelectromagnetic energy such as, for example, electric motors (e.g., mudpumps, traveling block, rotary table), coherent noise (e.g, stationarynoise such as from power lines, or rig activities), broadband noises,impulsive noises (e.g., banging or other perturbations), well heads, ACunits, vehicles, welding operations, consumer electronics, electricperturbations from external sources for which no direct mitigation canbe achieved, etc.

In accordance with the present techniques, signal 208 with accompanyingnoise is received by sensors, such as sensors 140 which providemeasurements 212 corresponding to signal 208 with accompanying noise, todemodulator 206 to be demodulated before the demodulated signals arepassed to a noise reduction manager 214.

In some embodiments, in addition to noise reduction techniques usingsensor measurements, noise reduction techniques using virtualmeasurements may also be used. Noise reduction using virtualmeasurements may address coherent noise sources, such as from stationarynoise sources or from rig activities. For example, a spectrogram 1240 inFIG. 14 includes a noisy measurement having a coherent noise around 8Hz.

Coherent noise sources n[m] may be mathematically represented accordingto the relationship below:

${n\lbrack m\rbrack} = {{A\lbrack m\rbrack} \cdot {\exp\left\lbrack {i\left( {{\frac{2\;\pi}{f_{s}}{\sum\limits_{p = 0}^{m}{f\lbrack p\rbrack}}} + \varphi_{0}} \right)} \right\rbrack}}$where:i is the imaginary unitf_(s) is the sampling frequencyA[m] is the noise amplitudef[m] is the noise instantaneous frequencyIn some embodiments, amplitude A[m] may be measured. Even when amplitudeA[m] is difficult to measure, the instantaneous frequency f[m] may beobtained, either using indirect measurements or advanced signalprocessing techniques. For example, a graph in FIG. 15 graphs aninstantaneous frequency f[m] of the coherent noise in the spectrogram inFIG. 14.

Estimation of f[m] Using Indirect Measurements

Coherent noise may be evaluated by estimating f[m] based on theelectromagnetic nature of electric motors used in drilling operations.For example, mud pumps, travelling blocks, and rotary tables used indrilling operations have electrical motors which use electrical energyat the stator to create an oriented rotating electromagnetic field withinstantaneous frequency f[m] and creates a rotating torque on thestator. The operation of the electrical motor induces variations in theelectromagnetic field. In some embodiments, the motor revolution speedmay be measured (e.g., using encoders or proximity sensors) to determinethe instantaneous frequency f[m]. Further, in some embodiments, theelectromagnetic field itself can be determined by measuring the backelectromotive force, coil, or directional magnetometer placed near themotor.

Referring again to FIG. 13, coherent noise n[m] 1212 may be used todetermine the instantaneous frequency f[m]. For example, an estimator1214 may be used to estimate an instantaneous frequency f[m] 1216 fromthe coherent noise n[m], based on the operational parameters of theelectrical motor.

Estimation of f[m] Using Signal Processing

Coherent noise may also be evaluated by estimating f[m] by applyingsignal processing algorithms. For example, any number of suitable signalprocessing algorithms, such as a recursive least squares (RLS)estimator, a Kalman filter, a constant modulus, etc., may be used aloneor in combination to estimate the instantaneous frequency f[m] from thecoherent noise n[m]. Moreover, in some embodiments, suitable signalprocessing algorithms may be applied to indirect measurements as well.Indirect measurements may be separately processed, or processed inconjunction with direct measurements.

Referring again to FIG. 13, the estimator 1214 may apply suitable signalprocessing algorithms on the coherent noise n[m] 1212 to estimate theinstantaneous frequency f[m]. For example, an estimator 1214 may be usedto estimate an instantaneous frequency f[m] 1216 by applying suitablesignal processing algorithms on the coherent noise n[m].

Virtual Channel

Once an estimate of f[m] is obtained, a virtual measurement 1220 may becreated by a virtual measurement generator 1218 or any suitableprocessor or controller. In some embodiments, two virtual measurementchannels may be created, where v_(i) [m] is the in-phase measurement andv_(g) [m] is the quadrature measurement, as represented according to therelationship below:

${v_{i}\lbrack m\rbrack} = {\exp\left\lbrack {i\left( {\frac{2\;\pi}{f_{s}}{\sum\limits_{p = 0}^{m}{f\lbrack p\rbrack}}} \right)} \right\rbrack}$${v_{q}\lbrack m\rbrack} = {\exp\left\lbrack {i\left( {{\frac{2\;\pi}{f_{s}}{\sum\limits_{p = 0}^{m}{f\lbrack p\rbrack}}} + \frac{\pi}{2}} \right)} \right\rbrack}$A spectrogram of the in-phase virtual measurement is provided in FIG.16. The in-phase and quadrature channels may then be converted tobaseband signals and used together at the input of the noise reductionmanager 214 b.

In one possible aspect, a sensor array noise reduction manager 214 canbe employed to apply the concepts of sensor array noise reduction toremove or reduce noise from the baseband signal to produce a denoisedsignal. The sensor array noise reduction manager 214 b in the globaluplink chain 1210 may have a failsafe architecture, such that if thereare large mismatches between the virtual channels and noise observed onthe direct measurements, the virtual channels may be eliminated by noisecancellation algorithms in the sensor array noise reduction manager 214b. Further, two or more virtual channels per noise component may be usedif there are multiple estimates of instantaneous frequency f[m]available from the estimation algorithms and/or the indirectmeasurements.

In some embodiments, decoded information 203 can be obtained from thedenoised signal by a symbol estimator 216 using any symbol estimationtechniques known in the art.

Example Computing Device

FIG. 17A shows an example device 1300, with a processor 1302 and memory1304 for hosting a sensor array noise reduction manager 1306 configuredto implement various embodiments of sensor array noise reduction asdiscussed in this disclosure. Memory 1304 can also host one or moredatabases, such as demixing vector database 1008, and can include one ormore forms of volatile data storage media such as random access memory(RAM)), and/or one or more forms of nonvolatile storage media (such asread-only memory (ROM), flash memory, and so forth).

Device 1300 is one example of a computing device or programmable device,and is not intended to suggest any limitation as to scope of use orfunctionality of device 1300 and/or its possible architectures. Forexample, device 1300 can comprise one or more computing devices,programmable logic controllers (PLCs), etc.

Further, device 1300 should not be interpreted as having any dependencyrelating to one or a combination of components illustrated in device1300. For example, device 1300 may include one or more of a computer,such as a laptop computer, a desktop computer, a mainframe computer,etc., or any combination or accumulation thereof.

As shown in FIG. 17B, device 1300 can also include a bus 1308 configuredto allow various components and devices, such as processors 1032, memory1304, and local data storage 1310, among other components, tocommunicate with each other. Bus 1308 can include one or more of any ofseveral types of bus structures, including a memory bus or memorycontroller, a peripheral bus, an accelerated graphics port, and aprocessor or local bus using any of a variety of bus architectures. Bus1308 can also include wired and/or wireless buses.

Local data storage 1310 can include fixed media (e.g., RAM, ROM, a fixedhard drive, etc.) as well as removable media (e.g., a flash memorydrive, a removable hard drive, optical disks, magnetic disks, and soforth).

A input/output (I/O) device 1312 may also communicate via a userinterface (UI) controller 1314, which may connect with I/O device 1312either directly or through bus 1308.

In one possible implementation, a network interface 1316 may communicateoutside of device 1300 via a connected network, and in someimplementations may communicate with hardware, such as one or moresensors 140, etc.

In one possible embodiment, sensors 140 may communicate with system 1300as input/output devices 1312 via bus 1308, such as via a USB port, forexample.

A media drive/interface 1318 can accept removable tangible media 1320,such as flash drives, optical disks, removable hard drives, softwareproducts, etc. In one possible implementation, logic, computinginstructions, and/or software programs comprising elements of the sensorarray noise reduction manager 1306 may reside on removable media 1320readable by media drive/interface 1318.

In one possible embodiment, input/output devices 1312 can allow a userto enter commands and information to device 1300, and also allowinformation to be presented to the user and/or other components ordevices. Examples of input devices 1312 include, for example, sensors, akeyboard, a cursor control device (e.g., a mouse), a microphone, ascanner, and any other input devices known in the art. Examples ofoutput devices include a display device (e.g., a monitor or projector),speakers, a printer, a network card, and so on.

In one possible implementation, sensor array noise reduction manager1306 can include a noise reduction module 1322 configured to, forexample, remove noise associated with one or more noise sources from areceived signal through use of a demixing vector. Various processes ofsensor array noise reduction manager 1306 may be described herein in thegeneral context of software or program modules, or the techniques andmodules may be implemented in pure computing hardware. Softwaregenerally includes routines, programs, objects, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. An implementation of these modules andtechniques may be stored on or transmitted across some form of tangiblecomputer-readable media. Computer-readable media can be any availabledata storage medium or media that is tangible and can be accessed by acomputing device. Computer readable media may thus comprise computerstorage media. “Computer storage media” designates tangible media, andincludes volatile and non-volatile, removable and non-removable tangiblemedia implemented for storage of information such as computer readableinstructions, data structures, program modules, or other data. Computerstorage media include, but are not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other tangiblemedium which can be used to store the desired information, and which canbe accessed by a computer.

Although a few embodiments of the disclosure have been described indetail above, those of ordinary skill in the art will readily appreciatethat many modifications are possible without materially departing fromthe teachings of this disclosure. Accordingly, such modifications areintended to be included within the scope of this disclosure as definedin the claims.

The invention claimed is:
 1. A sensor array noise reduction system forelectromagnetic telemetry with a downhole telemetry system, the sensorarray noise reduction system comprising: a plurality of sensors disposedto detect an electromagnetic telemetry signal and located at a wellsite,a noise reduction manager in communication with the plurality ofsensors, the noise reduction manager including one or more processorsand a non-transitory tangible computer-readable memory coupled to theone or more processors and having executable computer code storedthereon, the code comprising a set of instructions that causes one ormore processors to perform the following: receive electromagnetictelemetry measurement data from the plurality of sensors located at thewellsite; generate at least one virtual measurement signal based on aknown coherent noise or based on coherent noise measurement data;generate symbols from the electromagnetic telemetry measurement data andthe at least one virtual measurement signal; evaluate a dispersion ofsymbols generated from the electromagnetic telemetry measurement dataand the at least one virtual measurement signal around at least oneconstellation center in a constellation domain; select a demixing vectorthat reduces the dispersion below a predetermined threshold; and processthe selected demixing vector to remove electromagnetic noise from theelectromagnetic telemetry measurement data to produce a denoised signalthereby improving a signal to noise ratio of the electromagneticmeasurement data.
 2. The system of claim 1, wherein the instructionsfurther cause the one or more processors to estimate an instantaneousfrequency based on a rotational frequency of an electrical motor in thesystem, the at least one virtual measurement signal being computed basedon the instantaneous frequency.
 3. The system of claim 2, wherein theinstructions cause the one or more processors to estimate theinstantaneous frequency based on the rotational frequency of theelectrical motor by measuring the revolution speed of the electricalmotor using one or more encoders, proximity sensors, or combinationsthereof.
 4. The system of claim 1, wherein the instructions furthercause the one or more processors to estimate an instantaneous frequencybased on a variation of an electromagnetic field induced by a rotationof an electrical motor in the system, the at least one virtualmeasurement signal being computed based on the instantaneous frequency.5. The system of claim 4, wherein the instructions cause the one or moreprocessors to estimate the instantaneous frequency based on thevariation of the electromagnetic field by measuring the electromagneticfield using a coil, a directional magnetometer, or both.
 6. The systemof claim 4, wherein the instructions cause the one or more processors toestimate the instantaneous frequency based on the variation of theelectromagnetic field by measuring an electromotive force of theelectrical motor.
 7. The system of claim 1, wherein the instructionsfurther cause the one or more processors to estimate an instantaneousfrequency by applying one or more algorithms to the coherent noisemeasurement data, the at least one virtual measurement signal beingcomputed based on the instantaneous frequency.
 8. The system of claim 7,wherein the instructions cause the one or more processors to estimatethe instantaneous frequency by applying a recursive least squares (RLS)estimator to the coherent noise measurement signals.
 9. The system ofclaim 7, wherein the instructions cause the one or more processors toestimate the instantaneous frequency by applying a Kalman filter to thecoherent noise measurement signals.
 10. The system of claim 7, whereinthe instructions cause the one or more processors to estimate theinstantaneous frequency by applying a constant modulus to the coherentnoise measurement signals.
 11. The system of claim 1, wherein theinstructions further cause the one or more processors to generate thevirtual measurement signal based on an instantaneous frequency bycreating first and second virtual measurement signal channels, the firstand second virtual measurement signal channels being computed based onthe instantaneous frequency.
 12. The system of claim 11, wherein theinstructions cause the one or more processors to create first and secondvirtual measurement signal channels comprising a first in-phasemeasurement and a second quadrature measurement.
 13. A method forreducing electromagnetic noise in an electromagnetic telemetry systemfor use with a downhole telemetry system, the method comprising:receiving electromagnetic telemetry measurement data from a plurality ofsensors disposed at a wellsite to detect an electromagnetic telemetrysignal transmitted from a downhole tool; generating at least one virtualmeasurement signal based on a known coherent noise or based on coherentnoise measurement data; generating symbols from the electromagnetictelemetry measurement data and the at least one virtual measurementsignal; evaluating a dispersion of the symbols generated from theelectromagnetic telemetry measurement data and the at least one virtualmeasurement signal around at least one constellation center in aconstellation domain; selecting a demixing vector that reduces thedispersion below a predetermined threshold; and processing the selecteddemixing vector to remove electromagnetic noise from the electromagnetictelemetry measurement data to produce a denoised signal therebyimproving a signal to noise ratio in the electromagnetic telemetrysystem.
 14. The method of claim 13, further comprising: estimating aninstantaneous frequency of a noise source based on the known coherentnoise or based on the coherent noise measurement data; and processingthe instantaneous frequency to compute the at least one virtualmeasurement signal.
 15. The method of claim 14, wherein estimating theinstantaneous frequency is based on rotation of an electric motor in thesystem.
 16. The method of claim 13, wherein the at least one virtualmeasurement signal comprises first and second virtual measurementsignals, a first in-phase virtual measurement signal and a secondquadrature virtual measurement signal.
 17. The method of claim 16,wherein the first and second virtual measurement signals are convertedto baseband signals and processed in combination with theelectromagnetic telemetry measurement data to evaluate the dispersion.18. The method of claim 14, the instantaneous frequency is estimated byapplying a recursive least squares (RLS) estimator, a Kalman filter, ora constant modulus to the coherent noise measurement signals.